Name
Abstract | ||
|---|---|---|
This paper describes a supervised learning algorithm which optimizes a feature representation for temporally constrained clustering. The proposed method is applied to music segmentation, in which a song is partitioned into functional or locally homogeneous segments (e.g., verse or chorus). To facilitate abstraction over multiple training examples, we develop a latent structural repetition feature, which summarizes the repetitive structure of a song of any length in a fixed-dimensional representation. Experimental results demonstrate that the proposed method efficiently integrates heterogeneous features, and improves segmentation accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854594 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
acoustic signal processing,learning (artificial intelligence),music,time series,feature representation,latent structural repetition feature,music segmentation,ordinal linear discriminant analysis,supervised learning algorithm,temporally constrained clustering,Music,automatic segmentation,learning | Pattern recognition,Ordinal number,Computer science,Homogeneous,Segmentation,Supervised learning,Constrained clustering,Artificial intelligence,Supervised training,Linear discriminant analysis,Feature learning,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 13 | 1.00 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Mcfee | 1 | 440 | 24.05 |
| Daniel P. W. Ellis | 2 | 4198 | 356.08 |